title: Statistical modeling of the atrioventricular node during atrial fibrillation: data length and estimator performance.
abstract: The atrioventricular (AV) node plays a central role during atrial fibrillation (AF). We have recently proposed a statistical AV node model defined by parameters characterizing the arrival rate of atrial impulses, the probability of an impulse choosing either one of the dual AV nodal pathways, the refractory periods of the pathways, and the prolongation of refractory periods. All model parameters are estimated from the RR series using maximum likelihood (ML) estimation, except for the mean arrival rate of atrial impulses which is estimated by the AF frequency derived from the f-waves. The aim of this study is to present a unified approach to ML estimation which also involves the shorter refractory period, thus avoiding our previous Poincaré plot analysis which becomes biased. In addition, the number of RR intervals required for accurate parameter estimation is presented. The results show that the shorter refractory period can be accurately estimated, and that the resulting estimates converge to the true values when about 500 RR intervals are available.
authors.forename: Valentina D A
authors.initials: VD
authors.lastname: Corino
authors.forename: Frida
authors.initials: F
authors.lastname: Sandberg
authors.forename: Luca T
authors.initials: LT
authors.lastname: Mainardi
authors.forename: Leif
authors.initials: L
authors.lastname: Sornmo
mesh_terms: D000465:Algorithms; D001281:Atrial Fibrillation / Q000503:physiopathology*; D001283:Atrioventricular Node / Q000503:physiopathology*; D006801:Humans; D015233:Models, Statistical; D011336:Probability